Artificial Intelligence

Neutrosophy in Situation Analysis

In situation analysis (SA), an agent observing a
scene receives information from heterogeneous sources of information
including for example remote sensing devices, human reports
and databases. The aim of this agent is to reach a certain
level of awareness of the situation in order to make decisions. For
the purpose of applications, this state of awareness can be conceived
as a state of knowledge in the classical epistemic logic
sense. Considering the logical connection between belief and
knowledge, the challenge for the designer is to transform the raw,
imprecise, conflictual and often paradoxical information received
from the different sources into statements understandable by both
man and machines. Hence, quantitative (i.e. measuring the world)
and qualitative (i.e. reasoning about the structure of the world)
information processing coexist in SA. A great challenge in SA
is the conciliation of both aspects in mathematical and logical
frameworks. As a consequence, SA applications need frameworks
general enough to take into account the different types of uncertainty
and information present in the SA context, doubled with a
semantics allowing meaningful reasoning on situations. The aim
of this paper is to evaluate the capacity of neutrosophic logic and
Dezert- Smarandache theory (DSmT) to cope with the ontological
and epistemological problems of SA.

Submission history

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